Сравнение на методи

	Многопериодна обратна вероятностна претеглена оценка	Регресия с обратна вероятностна претеглена оценка за панелни данни
Област	Причинно-следствено заключение	Причинно-следствено заключение
Семейство	Regression model	Regression model
Година на възникване	2000	2000
Създател	Robins, Hernan & Brumback	Robins, Hernan & Brumback
Тип	Weighted causal estimator	Reweighting / causal inference
Основополагащ източник	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Други названия	longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW	panel IPW, longitudinal IPW, time-varying IPW, panel IPTW
Свързани	6	5
Резюме	Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population where treatment at each period is independent of measured confounders, enabling unbiased estimation of sustained treatment strategies.	Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods.